Vehicles operating in an autonomous mode (e.g., driverless) can relieve occupants, especially the driver, from some driving-related responsibilities. When operating in an autonomous mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.
Recognizing varies types of obstacles with a three-dimensional (3D) light detect and range (LIDAR) is a critical task for autonomous driving. Machine learning techniques are often adopted for such tasks. To improve the recolonization accuracy, a huge amount of data is needed to train machine learning algorithms. How to quickly and automatically obtain such data is a key issue. Currently, people manually label the obstacles on road based on video or 3D LIDAR images. However, such an approach consumes a huge amount of time for people to label the obstacles in the 3D LIDAR data points. Manually labeling the LIDAR data points is error prone. People may not be able to clearly recognize the obstacle on a 3D LIDAR image.